To solve the registration problem, the most popular solution is the iterative closest point (ICP) algorithm [1]}. In this approach, the rigid registration problem can be formulated into a least square (LS) function, which can be solved by establishing the point correspondences and calculating the rigid transformation iteratively. Although the ICP algorithm can achieve the rigid registration with good accuracy and efficiency, it cannot be applied to the registration of partially overlapping point sets. To trim the outliers, Chetverikov et al. [2]} introduced an overlap parameter into the LS function, which can be solved by the trimmed ICP (TrICP) algorithm. By discarding outliers, this approach can obtain accurate registration results for partially overlapping point sets. Nevertheless, it is time-consuming to calculate the overlap parameter. To accelerate the registration, Phillips et al. [3]} proposed the fractional TrICP (FTrICP) algorithm, which can compute the overlap parameter and the optimal transformation simultaneously. As the variants of ICP algorithm are well known to be susceptible to local convergence, the particle filter [4]} or genetic algorithm [5]} could be utilized to obtain the desired global minimum. Besides, effective features [6]}, [7]}, [8]} can also be extracted and matched for the point sets to be registered so as to provide the initial parameters for registration approaches. These registration approaches may obtain accurate results for the rigid registration, but they may not achieve the scaling registration, which may exist in some practical applications.
